kasra-keshavarz/mapply

Sensible multi-core apply/applymap functions for Pandas

mapply

mapply provides sensible multi-core apply/map/applymap functions for Pandas.

mapply vs. pandarallel vs. swifter

Where pandarallel only requires dill (and therefore has to rely on in-house multiprocessing and progressbars), swifter relies on the heavy dask framework, converting to Dask DataFrames and back. In an attempt to find the golden mean, mapply is highly customizable and remains lightweight, leveraging the powerful pathos framework, which shadows Python's built-in multiprocessing module using dill for universal pickling.

Installation

This pure-Python, OS independent package is available on PyPI:

$ pip install mapply

Usage

For documentation, see mapply.readthedocs.io.

import pandas as pd
import mapply

mapply.init(
    n_workers=-1,
    chunk_size=100,
    max_chunks_per_worker=8,
    progressbar=False
)

df = pd.DataFrame({"A": list(range(100))})

# avoid unnecessary multiprocessing:
# due to chunk_size=100, this will act as regular apply.
# set chunk_size=1 to skip this check and let max_chunks_per_worker decide.
df["squared"] = df.A.mapply(lambda x: x ** 2)

Development

Run make help for options like installing for development, linting, testing, and building docs.